Data Modelling
In this two day ABIS training, participants will learn how to create data models for relational databases, focusing on implementation for OLTP applications.
From understanding the basics of entity-relationship modelling to applying normalization and optimization techniques, participants will gain an understanding of the entire data modelling process.
This course will enable participants
- to master the techniques needed to build data models
- to apply key data modelling design principles through classic entity-relationship notation (“crow’s foot” notation)
- to build semantically accurate data models consisting of entities, attributes, relationships, hierarchies, and other modelling constructs
- to distinguish between conceptual modelling, logical modelling, and physical modelling
Schedule a training?
Delivered as a live, interactive training: available in-person or online, or in a hybrid format. Training can be implemented in English, Dutch, or French.
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Publieke opleidingskalender
Momenteel zijn er voor deze cursus geen publieke sessies gepland. Graag organiseren we een bedrijfssessie voor u of een extra publieke sessie (bij voldoende belangstelling). Geïnteresseerd? Laat het ons weten.
Intended for
Business analysts, data engineers, data analysts, database designers, or database administrators who need best practices and techniques for data modelling.
Background
Participants only need a basic understanding of data management concepts and constructs such as relational database tables and how different pieces of data logically relate to one another. No other prerequisites are needed.
Main topics
- Introduction to Data Modelling – the need for data modelling
- Data modelling – essential part of a Bigger Picture
- Understand the importance of data modelling and its role in database design
- Entity-Relationship Modelling (ERD)
- Creating Entity-Relationship Diagrams (ERDs) to represent the structure and relationships of data
- Labs
- Normalization Techniques
- The principles of normalization (1NF to 5NF; BCNF & 3NF); how to apply these techniques to eliminate redundancy and ensure data integrity
- The issue of denormalization
- Labs
- Conceptual, Logical, and Physical Models
- The differences between conceptual, logical, and physical data models – and the need for all three
- Creating conceptual, logical data, physical data base models: differences and similarities
- From concepts & properties to entities and attributes: deriving a logical data model from a conceptual model
- From entities and attributes to tables and columns: deriving a physical database model from a logical data model
- The importance of domains, keys, constraints, indexes, views, data types, ...
- Labs
- Best Practices and Guidelines
- Modelling industry best practices – naming conventions, documentation standards, design considerations, ...
- The importance of standards within the context of corporate data models
- The impact of enhanced security, auditing and traceability on model development
- Labs
- Data Model Documentation
- The importance of choosing relevant and readable names
- How data semantics can be partially derived from a well designed model
- How to document data models effectively, including entity definitions, attribute details, relationships, and constraints
- Examples
- Modelling Notations and tools
- Modelling notations (IE, Crows Foot, UML, Chen, ...)
- Modelling tools (ERwin, Oracle SQL Data Modeler, Lucid Chart, SqlDBM, IBM Rational tooling, ...)
- Demo
- What about other application types – an introduction
- OLTP versus OLAP
- Dimensional Modelling – data warehouses – analytical databases
- Star models, snowflake models
- NoSQL databases
- Demo
- Case study
Training method
Live instructor-led training, with plenty of opportunities for hands-on exercises and discussion.
Certificate
At the end of the session, the participant receives a 'Certificate of Completion'.
Duration
2 days.
Course leader
Peter Vanroose (ABIS)
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